Lab and skill programs

Which topic covers each lab session and skill session, and what to build for each.

Each session below points to a topic with a program you can run on the page. Run it, understand it, then do the extension as your lab work.

Session	Task	Go to	Extension to build
1	Linear regression	Topic 28	Add a second input feature, and report MSE and R²
2	Classification with decision trees	Topic 4, Topic 30	Run your ID3 on a 6-row dataset of your own
3	Decision tree with evaluation parameters	Topic 30	Calculate precision, recall and F1 by hand from the confusion matrix, and compare with the library
4	Decision tree construction	Topic 6, Topic 30	Compare entropy and Gini on `load_wine`
5	Random forest classification	Topic 31	Plot accuracy against the number of trees
6	Naive Bayes on adult census data	Topic 32	Run it on the real Adult data on your own computer
7	K-nearest neighbours	Topic 33	Implement weighted voting
8	K-means clustering	Topic 34, Topic 13	Cluster `load_wine`, and describe each cluster
9	Perceptron	Topic 15	Train NAND and NOR gates
10	ANN model with a genetic optimiser	Topic 35	Try different mutation sizes and report the results
11	Multilayer network to predict the weather	Topic 35	Compare three network sizes, and draw a learning curve
12	ANN classification model	Topic 17, Topic 35	Apply an MLP to `load_breast_cancer`, and report precision and recall

Session	Task	Go to
1	Data exploration with pandas and visualisation	Topic 26
2	Data preprocessing	Topic 27
3	Build a linear regression model	Topic 28
4	Decision boundary of a linear regression	Topic 28 (second program)
5	Handling missing values, then linear regression	Topic 28 (second program)
6	Build a decision tree	Topic 4, Topic 30
7	Post-pruning a decision tree	Topic 30, Topic 6
8	Naive Bayes as a generative classifier	Topic 11
9	Predict the income class with naive Bayes	Topic 32
10	Build a Bayesian belief network	Topic 12
11	AND and OR gates with a perceptron	Topic 15
12	Neural network with one hidden layer of 2 neurons	Topic 16, Topic 17
13	Multilayer network using backpropagation	Topic 17
14	The TensorFlow environment	Project guide (on your own computer)
15	Gradient descent for logistic regression	Topic 29
16	Genetic algorithm to build a model	Topic 18, Topic 35
17–24	Case studies and the project	Project guide